The present embodiments relate to tree matching in medical imaging. Deformable point-set matching is performed for medical image analysis and computer vision. Tree matching arises in a variety of situations, such as for coronary tree matching for enhanced multiphase cardiac CT reconstruction and lung airway tree matching. The goal is to match the tree at one point in time to the tree at another point in time. The task may be difficult to implement with a computer and too data intensive to implement manually.
In “Point Set Registration: Coherent Point Drift,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (2010) 2262-2275, Myronenko, et al. introduced a coherence point drift (CPD) approach to tree matching. The regularization term in the deformable (non-rigid) version of the CPD algorithm is based upon the motion coherency theory. CPD uses the probabilistic, outlier-resistant Gaussian mixture model (GMM) and expectation-maximization framework to find a transform from one tree to another. The GMM model provides a probabilistic version of least-squares point matching terms. However, there is some inaccuracy in the tree matching using CPD. A more accurate transform may assist medical professionals.